{"date":"2026-05-04","schema_version":1,"captured_at":"2026-08-25T23:07:18.469Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":14},"attention":[{"title":"Michael Jackson","views":307489,"rank":4,"extract":"Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the \"King of Pop\", he is widely regarded as one of the most culturally significant figures of the 20th century. 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GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, sev","authors":["Longa, Antonio","Azzolin, Steve","Santin, Gabriele","Cencetti, Giulia","Liò, Pietro","Lepri, Bruno"],"url":"https://arxiv.org/abs/2210.15304","arxiv_id":"2210.15304","published":"2026-05-04"},{"title":"Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms","abstract":"We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of their Frobenius norms and positive lineal entrywise sums, depending on the type of tensor network involved. The core of this method is the use of the norm of subnetworks of the tensor network in an iterative way, so that we normalize by the finite values of the norms that le","authors":["Ali, Alejandro Mata","Delgado, Iñigo Perez","Roura, Marina Ristol","de Leceta, Aitor Moreno Fdez."],"url":"https://arxiv.org/abs/2309.06577","arxiv_id":"2309.06577","published":"2026-05-04"},{"title":"Value Explicit Pretraining for Learning Transferable Representations","abstract":"Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose \\textit{Value Explicit Pretraining} (VEP), a method that learns generalizable representations for transfer reinforcement learning. VEP enables efficient learning of new tasks that share similar objectives as previously learned tasks, by learning an encoder that trains represe","authors":["Lekkala, Kiran","Bao, Henghui","Sontakke, Sumedh A.","Biyik, Erdem","Itti, Laurent"],"url":"https://arxiv.org/abs/2312.12339","arxiv_id":"2312.12339","published":"2026-05-04"},{"title":"Koopman-Assisted Reinforcement Learning","abstract":"The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theory. However, these equations become intractable for high-dimensional or nonlinear systems. This paper develops two new reinforcement learning algorithms based on the data-driven Koopman operator, which lifts a nonlinear system into new coordinates where the dynamics become appro","authors":["Rozwood, Preston","Mehrez, Edward","Paehler, Ludger","Sun, Wen","Brunton, Steven L."],"url":"https://arxiv.org/abs/2403.02290","arxiv_id":"2403.02290","published":"2026-05-04"},{"title":"AI Consciousness is Inevitable: A Theoretical Computer Science Perspective","abstract":"We look at consciousness through the lens of Theoretical Computer Science, a branch of mathematics that studies computation under resource limitations, distinguishing functions that are efficiently computable from those that are not. From this perspective, we develop a formal machine model for consciousness. The model is inspired by Alan Turing's simple yet powerful model of computation and Bernard Baars' the","authors":["Blum, Lenore","Blum, Manuel"],"url":"https://arxiv.org/abs/2403.17101","arxiv_id":"2403.17101","published":"2026-05-04"},{"title":"Laissez-Faire Harms: Algorithmic Biases in Generative Language Models","abstract":"The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown that discrimination can occur even when identity terms are ","authors":["Shieh, Evan","Vassel, Faye-Marie","Sugimoto, Cassidy","Monroe-White, Thema"],"url":"https://arxiv.org/abs/2404.07475","arxiv_id":"2404.07475","published":"2026-05-04"},{"title":"Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications","abstract":"Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tasks (which lack standard benchmarks) make general-purpose dataset practices a poor fit. Visual Wake Words (VWW), the prior standard TinyML person detection benchmark, contains roughly 123K images and has an estimated label error rate of 7.8%","authors":["Banbury, Colby","Njor, Emil","Garavagno, Andrea Mattia","Mazumder, Mark","Stewart, Matthew","Warden, Pete"],"url":"https://arxiv.org/abs/2405.00892","arxiv_id":"2405.00892","published":"2026-05-04"},{"title":"A Survey on Vision-Language-Action Models for Embodied AI","abstract":"Embodied AI is widely recognized as a cornerstone of artificial general intelligence (AGI) because it involves controlling embodied agents to perform tasks in the physical world. Building on the success of large language models (LLMs) and vision-language models (VLMs), a new category of multimodal models -- referred to as vision-language-action (VLA) models -- has emerged to address language-conditioned robotic tasks","authors":["Ma, Yueen","Song, Zixing","Zhuang, Yuzheng","Hao, Jianye","King, Irwin"],"url":"https://arxiv.org/abs/2405.14093","arxiv_id":"2405.14093","published":"2026-05-04"},{"title":"Image Score: Learning and Evaluating Human Preferences for Mercari Search","abstract":"Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to discover desired items, we have always had a substantial amount of data with implicit feedback. Although we actively take advantage of that to provide the best service for our users, the correlation of implicit feedback for such tasks as image quality assessment is not triv","authors":["Oinar, Chingis","Cao, Miao","Fu, Shanshan"],"url":"https://arxiv.org/abs/2408.11349","arxiv_id":"2408.11349","published":"2026-05-04"},{"title":"Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs","abstract":"This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regularized Accelerated Natural Policy Gradient (PDR-ANPG) algorithm that uses entropy and quadratic regularizers to reach this goal. For parameterized policy classes with a transferred compatibility approximation error, $ε_{\\mathrm{bias}}$, PDR-ANPG achieves a last-iterate $ε","authors":["Mondal, Washim Uddin","Aggarwal, Vaneet"],"url":"https://arxiv.org/abs/2408.11513","arxiv_id":"2408.11513","published":"2026-05-04"},{"title":"A Unified Deep Learning Framework for Motion Correction in Medical Imaging","abstract":"Deep learning has shown significant value in medical image registration for motion correction, however, current techniques are either limited by the type and range of motion they can handle, or require iterative inference and/or retraining for new imaging data. To address these limitations, we introduce UniMo, a Unified Motion Correction framework that leverages deep neural networks to correct for various types of mo","authors":["Wang, Jian","Faghihpirayesh, Razieh","Joca, Danny","Golland, Polina","Gholipour, Ali"],"url":"https://arxiv.org/abs/2409.14204","arxiv_id":"2409.14204","published":"2026-05-04"},{"title":"Machine Learning Toric Duality in Brane Tilings","abstract":"We apply a variety of machine learning methods to the study of Seiberg duality within 4d $\\mathcal{N}=1$ quantum field theories arising on the worldvolumes of D3-branes probing toric Calabi-Yau 3-folds. Such theories admit an elegant description in terms of bipartite tessellations of the torus known as brane tilings or dimer models. An intricate network of infrared dualities interconnects the space of such theories a","authors":["Capuozzo, Pietro","Gherardini, Tancredi Schettini","Suzzoni, Benjamin"],"url":"https://arxiv.org/abs/2409.15251","arxiv_id":"2409.15251","published":"2026-05-04"},{"title":"Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation","abstract":"Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep learning techniques and classic numerical methods for differential equations to address two challenging missing physics problems in dynamical systems theory: dynamics discovery and parameter estimation. The presented methods encode available information relating to the system","authors":["Ho, Caitlin","Arnold, Andrea"],"url":"https://arxiv.org/abs/2410.04299","arxiv_id":"2410.04299","published":"2026-05-04"},{"title":"PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance","abstract":"In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long videos through extremely extended context lengths. However, this comes at the cost of significantly increased computational overhead due to the massive number of visual tokens, making efficiency a major bottleneck. In this paper, we identify the root of this inefficiency as","authors":["Sun, Shangkun","Liu, Ruyang","Tang, Haoran","Ge, Yixiao","Lu, Haibo","Gao, Wei"],"url":"https://arxiv.org/abs/2411.02327","arxiv_id":"2411.02327","published":"2026-05-04"},{"title":"Bias in Large Language Models: Origin, Evaluation, and Mitigation","abstract":"Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks. The review critically assesses a range of bias evaluation ","authors":["Guo, Yufei","Guo, Muzhe","Su, Juntao","Yang, Zhou","Zhu, Mengqiu","Li, Hongfei"],"url":"https://arxiv.org/abs/2411.10915","arxiv_id":"2411.10915","published":"2026-05-04"},{"title":"Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey","abstract":"Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representations indistinguishable. Both phenomena stem from the interaction between message passing and the input topology, ultima","authors":["Attali, Hugo","Buscaldi, Davide","Pernelle, Nathalie","Malliaros, Fragkiskos D."],"url":"https://arxiv.org/abs/2411.17429","arxiv_id":"2411.17429","published":"2026-05-04"},{"title":"Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies","abstract":"Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the choice of instructions or prompts. To bypass the limitations of discrete text prompts, we formulate post-training adaptation as a latent control problem, where the goal embedding serves as a continuous control variable to modulate the behavior","authors":["Zhao, Guangyu","Lian, Kewei","Ru, Haoxuan","Zhang, Borong","Lin, Haowei","Mu, Zhancun"],"url":"https://arxiv.org/abs/2412.02125","arxiv_id":"2412.02125","published":"2026-05-04"},{"title":"TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems","abstract":"Efficient real-time solvers for forward and inverse problems are essential in engineering and science applications. Machine learning surrogate models have emerged as promising alternatives to traditional methods, offering substantially reduced computational time. Nevertheless, these models typically demand extensive training datasets to achieve robust generalization across diverse scenarios. While physics-based appro","authors":["Nguyen, Hai V.","Bui-Thanh, Tan","Dawson, Clint"],"url":"https://arxiv.org/abs/2412.07010","arxiv_id":"2412.07010","published":"2026-05-04"},{"title":"Representation in large language models","abstract":"The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical theorizing aimed at explaining how they do what they do. Unfortunately, disagreement over fundamental theoretical issues has led to stalemate, with entrenched camps of LLM optimists and pessimists often committed to very different views of how these systems work. Overcom","authors":["Yetman, Cameron"],"url":"https://arxiv.org/abs/2501.00885","arxiv_id":"2501.00885","published":"2026-05-04"},{"title":"Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty","abstract":"We develop a new class of distance-aware error bounds that tightly characterize the approximation error of spline neural networks. Our bottom-up approach analyzes the error bound of each neuron (a spline) and then extends it to the full network. We begin with error bounds for Newton's polynomial, generalize them to arbitrary splines under higher-order Lipschitz continuity, and extend the result to function compos","authors":["Ataei, Masoud","Khojasteh, Mohammad Javad","Dhiman, Vikas"],"url":"https://arxiv.org/abs/2501.04757","arxiv_id":"2501.04757","published":"2026-05-04"},{"title":"Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images","abstract":"The advancement of AI-assisted myopia screening necessitates the joint diagnosis of both-eye (OU) high myopia (HM) status and the prediction of axial length (AL). This clinical requirement introduces a complex mixed-type (binary-continuous) multitask learning task with bi-domain (OU) image covariates, giving rise to two key challenges: i) capture the inter-ocular asymmetry of OU images within a cutting-edge foundatio","authors":["Zhong, Chong","Liu, Yunhao","Li, Yang","Fu, Xiang","Yang, Jin","Yang, Danjuan"],"url":"https://arxiv.org/abs/2501.06540","arxiv_id":"2501.06540","published":"2026-05-04"},{"title":"Mean-field limit from general mixtures of experts to quantum neural networks","abstract":"In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main result establishes the propagation of chaos for a MoE as the number of experts diverges. We demonstrate that the corresponding empirical measure of their parameters is close to a probability measure that solves a nonlinear continuity equation, and we provide an explicit conver","authors":["Hernandez, Anderson Melchor","Pastorello, Davide","De Palma, Giacomo"],"url":"https://arxiv.org/abs/2501.14660","arxiv_id":"2501.14660","published":"2026-05-04"},{"title":"Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners","abstract":"We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian learners and no-regret learners who compete in markets and identifying the conditions under which each approach is more effective. We formally relate the notions of survival and market dominance studied in economics and the framework of regret minimization, thereby bridging the","authors":["Easley, David","Kolumbus, Yoav","Tardos, Eva"],"url":"https://arxiv.org/abs/2502.08597","arxiv_id":"2502.08597","published":"2026-05-04"},{"title":"Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers","abstract":"Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dynamic scene rendering. However, the challenge of natural-looking object insertion, where the object's appearance seamlessly matches the scene, remains unsolved. In this work, we propose a method, dubbed D3DR, for inserting a 3DGS-parametrized object into a 3DGS scene ","authors":["Skorokhodov, Vsevolod","Durasov, Nikita","Fua, Pascal"],"url":"https://arxiv.org/abs/2503.06740","arxiv_id":"2503.06740","published":"2026-05-04"},{"title":"Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium","abstract":"Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying these models for decision-making. In this paper, we seek to uncover fundamental statistical limits concerning aligning LLMs with human preferences, with a focus on the probabilistic representation of human preferences and the preservation of diverse preferences in aligned LLMs. W","authors":["Liu, Kaizhao","Long, Qi","Shi, Zhekun","Su, Weijie J.","Xiao, Jiancong"],"url":"https://arxiv.org/abs/2503.10990","arxiv_id":"2503.10990","published":"2026-05-04"}],"history":{"events":[{"year":2019,"text":"The inaugural race of the W Series took place at Hockenheimring, Germany, and was won by Jamie Chadwick (pictured), who later became the season's champion.","url":"https://en.wikipedia.org/wiki/2019_W_Series"},{"year":2015,"text":"The Parliament of Malta moved from the Grandmaster's Palace to the purpose-built Parliament House (pictured).","url":"https://en.wikipedia.org/wiki/Parliament_of_Malta"},{"year":2000,"text":"Ken Livingstone took office as the first mayor of London.","url":"https://en.wikipedia.org/wiki/Ken_Livingstone"},{"year":1982,"text":"Falklands War: HMS Sheffield was struck by an Exocet missile, killing 20 sailors and leading to its sinking six days later—the first Royal Navy ship sunk in action since World War II.","url":"https://en.wikipedia.org/wiki/Falklands_War"},{"year":1979,"text":"Margaret Thatcher became the first female prime minister of the United Kingdom.","url":"https://en.wikipedia.org/wiki/Margaret_Thatcher"},{"year":1974,"text":"An all-female Japanese team reached the summit of Manaslu in the Himalayas, becoming the first women to climb a peak higher than 8,000 metres (26,247 ft) above sea level.","url":"https://en.wikipedia.org/wiki/Manaslu"},{"year":1942,"text":"World War II: Aircraft from Imperial Japanese Navy vessels attacked Allied naval forces, beginning the Battle of the Coral Sea, the first naval action in which the participating ships never sighted or fired directly at each other.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1919,"text":"The May Fourth Movement began in China with large-scale student demonstrations in Tiananmen Square, Beijing.","url":"https://en.wikipedia.org/wiki/May_Fourth_Movement"},{"year":1886,"text":"During a labor rally in Chicago, a bomb explosion and gunfire led to the deaths of eight police officers and four members of the public.","url":"https://en.wikipedia.org/wiki/Labour_movement"},{"year":1789,"text":"The Boydell Shakespeare Gallery, part of a cultural project initiated by engraver John Boydell, opened in London.","url":"https://en.wikipedia.org/wiki/Boydell_Shakespeare_Gallery"},{"year":1776,"text":"American Revolution: The Colony of Rhode Island and Providence Plantations became the first of the Thirteen Colonies to renounce its allegiance to the British Crown.","url":"https://en.wikipedia.org/wiki/American_Revolution"},{"year":1677,"text":"Trunajaya rebellion: Dutch East India Company forces under Cornelis Speelman began an attack on Surabaya, on the island of Java.","url":"https://en.wikipedia.org/wiki/Trunajaya_rebellion"},{"year":1626,"text":"Having been appointed the director-general of New Netherland, Peter Minuit arrived in Manhattan.","url":"https://en.wikipedia.org/wiki/Directors_of_New_Netherland"},{"year":1493,"text":"Pope Alexander VI (pictured) issued the papal bull Inter caetera, establishing a line of demarcation dividing the New World between Spain and Portugal.","url":"https://en.wikipedia.org/wiki/Pope_Alexander_VI"}],"births":[{"year":2001,"text":"Noah Beck, American media personality and actor","url":"https://en.wikipedia.org/wiki/Noah_Beck"},{"year":2001,"text":"Joan García, Spanish footballer","url":"https://en.wikipedia.org/wiki/Joan_Garcia"},{"year":1998,"text":"Rex Orange County, English musician","url":"https://en.wikipedia.org/wiki/Rex_Orange_County"},{"year":1995,"text":"Shameik Moore, American actor and musician","url":"https://en.wikipedia.org/wiki/Shameik_Moore"},{"year":1994,"text":"Abi Masatora, Japanese sumo wrestler","url":"https://en.wikipedia.org/wiki/Abi_Masatora"},{"year":1994,"text":"Joseph Tapine, New Zealand rugby league player","url":"https://en.wikipedia.org/wiki/Joseph_Tapine"},{"year":1992,"text":"Victor Oladipo, American basketball player","url":"https://en.wikipedia.org/wiki/Victor_Oladipo"},{"year":1991,"text":"Brianne Jenner, Canadian women's ice hockey player","url":"https://en.wikipedia.org/wiki/Brianne_Jenner"}],"deaths":[{"year":2024,"text":"Tekin Kartal, Kurdish drug trafficker and gangster (born 1990)","url":"https://en.wikipedia.org/wiki/Tekin_Kartal"},{"year":2024,"text":"Ron Kavana, Irish singer, songwriter, guitarist and band leader (born 1950)","url":"https://en.wikipedia.org/wiki/Ron_Kavana"},{"year":2024,"text":"Frank Stella, American painter (born 1936)","url":"https://en.wikipedia.org/wiki/Frank_Stella"},{"year":2021,"text":"Nick Kamen, English model, songwriter (born 1962)","url":"https://en.wikipedia.org/wiki/Nick_Kamen"},{"year":2020,"text":"Don Shula, American football player and coach (born 1930)","url":"https://en.wikipedia.org/wiki/Don_Shula"},{"year":2020,"text":"Greg Zanis, American carpenter and activist (born 1950)","url":"https://en.wikipedia.org/wiki/Greg_Zanis"},{"year":2019,"text":"Alia Abdulnoor, Emirati imprisoned woman (born 1977)","url":"https://en.wikipedia.org/wiki/Alia_Abdulnoor"},{"year":2016,"text":"Jean-Baptiste Bagaza, Burundian politician (born 1946)","url":"https://en.wikipedia.org/wiki/Jean-Baptiste_Bagaza"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv 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Every item links to its source."}